Compressed sensing magnetization-prepared 2 rapid gradient-echo for accelerated hip cartilage T1 mapping and deep learning-based 3D segmentation of hip cartilage and labrum

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Authors: Julien Hirt, Jose Roshardt, Malin K. Meier, Adrian Cyril Ruckli, Till Dominic Lerch, Moritz Tannast, Simon Damian Steppacher, Gabriele Bonanno, Gianfranco Piredda, B. Jung, Florian Schmaranzer
Cited by: 1
References: 31

Abstract

BACKGROUND: Comprehensive assessment of hip cartilage morphology and composition is essential for surgical decision-making in femoroacetabular impingement and hip dysplasia. Magnetization-prepared 2 rapid gradient-echo (MP2RAGE) enables T1 mapping and segmentation but requires long acquisition times, limiting clinical adoption. To validate a compressed sensing (CS) MP2RAGE protocol against the standard sequence for hip cartilage T1 mapping and automated 3D segmentation of hip cartilage and labrum. METHODS: This retrospective study included 18 patients who underwent direct MR arthrography with both standard MP2RAGE (7 min 32 s) and CS-MP2RAGE (5 min). Automated segmentation of cartilage and labrum was performed using a 3D U-Net deep learning model to extract global and regional (anterior, superior, posterior) hip cartilage T1 values and 3D parameters of hip cartilage and labrum. Morphological damage was scored semi-quantitatively on clinical proton-density weighted MRI. Agreement was assessed using paired t-tests, Bland-Altman analysis, Dice similarity coefficients, and Spearman correlations. RESULTS: > 0.96). CONCLUSIONS: Compressed sensing MP2RAGE enables 5-min acquisition with strong agreement for cartilage T1 and automated morphological assessment compared with conventional MP2RAGE. Further prospective validation is needed before broader clinical implementation.

Fields of Study & Subjects

Osteoarthritis Treatment and Mechanisms Advanced MRI Techniques and Applications Ultrasound Imaging and Elastography

Publication details

Journal / source
Magnetic Resonance Imaging
Publisher
Elsevier BV
Volume
133
Pages
110725–110725
Publication date
8 June 2026
Language
EN